# Transformer Based Implementation for Automatic Book Summarization

arXiv: https://arxiv.org/abs/2301.07057 · HTML: https://mlsanity.com/paper/2301.07057/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 17 Jan 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [Recursively Summarizing Books with Human Feedback](https://mlsanity.com/paper/2109.10862/) (2021)
- [Leveraging BERT for Extractive Text Summarization on Lectures](https://mlsanity.com/paper/1906.04165/) (2019)
- [The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architectures](https://mlsanity.com/paper/2104.10640/) (2021)
- [Generating News Headlines with Recurrent Neural Networks](https://mlsanity.com/paper/1512.01712/) (2015)
- [Bidirectional Attentional Encoder-Decoder Model and Bidirectional Beam Search for Abstractive Summarization](https://mlsanity.com/paper/1809.06662/) (2018)
- [Efficient comparison of sentence embeddings](https://mlsanity.com/paper/2204.00820/) (2022)

## Built on by (most cited first, 2 of 2)

- [Core Building Blocks: Next Gen Geo Spatial GPT Application](https://mlsanity.com/paper/2310.11029/) (2023)
- [SALE : Low-bit Estimation for Efficient Sparse Attention in Long-context LLM Prefilling](https://mlsanity.com/paper/2505.24179/) (2025)

## Abstract (arXiv)

Document Summarization is the procedure of generating a meaningful and concise summary of a given document with the inclusion of relevant and topic-important points. There are two approaches: one is picking up the most relevant statements from the document itself and adding it to the Summary known as Extractive and the other is generating sentences for the Summary known as Abstractive Summarization. Training a machine learning model to perform tasks that are time-consuming or very difficult for humans to evaluate is a major challenge. Book Abstract generation is one of such complex tasks. Traditional machine learning models are getting modified with pre-trained transformers. Transformer based language models trained in a self-supervised fashion are gaining a lot of attention; when fine-tuned for Natural Language Processing(NLP) downstream task like text summarization. This work is an attempt to use Transformer based techniques for Abstract generation.